Most AI-powered search or knowledge retrieval tools rely on data indexing for fast and efficient data retrieval. This process typically involves copying data into a vector database, which indexes and stores numerical representations of the data.
However, despite its widespread use, data indexing has significant drawbacks, especially for enterprise use. These include reduced security, lower precision, and complex data management challenges.
AI-powered answers without indexing your data
The good news is that Qatalog offers a powerful alternative to traditional AI knowledge retrieval tools that index data. Its ActionQuery AI engine combines federated search (using real-time APIs) and retrieval augmented generation (RAG) to deliver several key benefits.
1. Data security
Qatalog ensures maximum security by not copying, indexing, or storing any retrieved data. Instead, information is pulled directly from the source in real time and discarded immediately after use. This eliminates the need for data transfer or duplication, significantly reducing security risks.
2. High accuracy
Qatalog generates the responses exclusively from your latest data which increases their accuracy and reduces hallucinations. Fetching AI answers without indexing your data ensures consistently accurate query resolutions even with large datasets, making Qatalog highly scalable.
3. Easier data management
With no data index to maintain, managing the data is much simpler. It also eliminates the need for constant updates from hundreds of tools.
Explore Qatalog for 14 days for free or book a live 1:1 demo with one of our AI knowledge specialists who can guide you through our product, security and enterprise support capabilities.
Challenges of data indexing for enterprises
Data indexing often presents a major entry barrier for enterprises. Vendors like Glean and its competitors store data copies on third-party servers, which increases security risks and the complexity of managing vast amounts of information. It can lead to inaccuracies and substantial overhead in maintaining these systems.
1. Reduced data-security
One major issue with AI knowledge management systems that index data data indexing for enterprises is the potential compromise of data security. Indexing creates copies of your data, usually stored on third-party servers. It may speed up some searches but also increases the potential attack surface for hackers and the potential damage of a data breach.
What’s more, instead of storing information on multiple distributed applications, indexes bring all that data together in one place. Ordinarily, if a specific app has a data-security problem, the damage is limited to the information stored within that app. But if the data-index provider is breached, the hackers could access a much larger range of data, including your indexed data.
Although vector embeddings might seem less risky because their numerical representations don’t hold clear meaning, a recent study from researchers at Cornell University found that 92% of the original meaning can be recovered exactly. The conclusion was that "embeddings should be treated with the same precautions as raw data".
2. Poor precision
Another issue with data-indexing is that precision and accuracy degrades significantly as datasets grow larger. This is because data-indexes use a vector database to index information.
Vector databases work by transforming the source information into a vectorized numerical form, where it is then stored in high-dimensional space. Nearest-neighbor algorithms are used to resolve searches, identifying the vector in the dataset with the smallest distance to the query vector and marking it as the nearest neighbor.
However, as more and more data is added, the volume of the space increases exponentially, and the data becomes increasingly sparse. This sparsity means that the distance between many data points converges in relative terms, making it difficult to distinguish between a genuine nearest neighbor and a non-relevant data point.
The practical consequence of this loss of precision is that the prevalence of false positives will increase. As a result the AI answers generated from indexed data may be grounded in real information, but could use the wrong information to generate the answer reduced precision.
This is different to the problem of hallucination where information in a large language model’s (LLM) training data is used to resolve queries, generating completely fabricated responses.
3. Complex data management
Another challenge with data indexing AI search tools is the complexity of managing data. Large enterprises generate vast amounts of data from numerous software applications, which complicates and increases the cost of data management Keeping the index up to date requires ongoing effort and coordination.
While solution providers help manage some of these tasks, significant responsibilities remain with the customer. This might include:
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Data mapping: Creating accurate mappings from diverse data sources to guide the indexing process requires a deep understanding of the data and its structure.
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Managing duplications: Handling duplicate information across different systems is critical to maintain the integrity and usefulness of the indexed data.
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Monitoring data pipelines: Monitoring the data pipelines for errors, latency, or other issues is crucial for maintaining the reliability of the system.
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Data cleaning: Data may need to be cleaned or transformed to ensure it’s consistent and high-quality, which is fundamental for the effectiveness of the solution.
Managing these aspects across a constantly evolving and expanding enterprise dataset can be daunting. Often, a dedicated team is needed to handle internal data systems, so that the indexing works as it should. This adds significant overheads to the real cost of the solution and potentially diverting resources from other important areas.